White Paper
Modernizing Motor Truck Cargo Underwriting Through Data Retention and Quantifiable Risk
Executive Summary
Motor truck cargo underwriting has long depended on subjective decision-making. Underwriters review applications, loss runs, safety scores, and operational details—but the process remains inconsistent, manual, and influenced by individual bias.
Critical submission data, including from declined and quote-not-taken (QNW) business, is routinely discarded. This destroys strategic insight and prevents insurers from learning from the full market picture.
Fleetidy introduces a modern underwriting engine that automates data ingestion, retains every submission, and produces a quantifiable, empirical equation of risk. The result is measurable underwriting, consistent pricing, and a real-time understanding of portfolio behavior.
Problems in Traditional Cargo Underwriting
Subjective Evaluation
Underwriters evaluate risk variables inconsistently, such as:
- FMCSA safety scores
- Fleet size and utilization
- Commodity hazard characteristics
- Geographic distribution
- Operational behavior
- Historical loss performance
Manual, Fragmented Data Review
Submissions arrive through portals, spreadsheets, email attachments, PDFs, and handwritten descriptions. Manual review introduces inefficiency and error.
Data Waste
Declined and QNW submissions disappear from underwriting systems, removing:
- harvestible data sets
- market intelligence
- opportunity trends
- validation of current strategies
Underwriting Bias
Without empirical measurement, the underwriting process relies on prior experience, memory, and heuristics. Bias becomes embedded in the portfolio and a function of employee attrition.
Complexity of Cargo Risk
Cargo loss exposure depends on multiple interacting variables:
- driver quality and safety performance (ISS, SAFER, etc)
- fleet size and structure (Owner Operators vs Hired Drivers)
- commodity mix and hazard level
- geographic footprint and route selection
- historical loss behavior as a predictor of future outcomes
Fleetidy: A Modern Underwriting Engine
Automated Data Capture
Fleetidy ingests and structures:
- FMCSA safety and inspection data
- loss history
- application details
- commodity categories
- fleet configuration
- geographic and routing information
Permanent Underwriting Intelligence Repository
Fleetidy retains bound accounts, declined submissions, QNW submissions, incomplete submissions, and repeated submissions over time. Every data point strengthens long-term strategy.
The FRED Score
The Fleet Risk Empirical Data score is a mathematically derived, explainable risk measure based on real-world empirical data.
Algorithmic Structure
Fleetidy evaluates six foundational underwriting variables:
- Safety performance
- Fleet size and structure
- Commodity mix
- Loss history
- Geography and route behavior
- Operational and production source patterns
Which become normalized across the FMCSA Census data and then compared to averages and historical empirical results
Retention of Declined and QNW Business
The industry’s greatest blind spot is the information it throws away. Most insurers retain only bound accounts, losing insight into:
- true market size
- producer submission patterns
- rejected but profitable opportunities
- geographic and commodity demand signals
- predictive trends in risk behavior
Fleetidy preserves these insights permanently, reducing bias and enabling strategic decision-making.
Mathematical Formulation of Risk
Below are equations showing the contrast between traditional underwriting and Fleetidy’s empirical model.
Traditional (Subjective) Underwriting Model
Risk is implicitly treated as the sum of the underwriter’s subjective estimations of:
\begin{equation} F_{\text{risk}} = f(\text{Safety}) + f(\text{Fleet Size}) + f(\text{Commodity Mix}) + f(\text{Loss History}) + f(\text{Geography}) + f(\text{Operations}) \end{equation}Fleetidy (Empirical, Coefficient-Weighted) Model
Fleetidy introduces real, data-driven coefficients:
\begin{equation} F_{\text{risk}} = A_1 \cdot \text{Safety} + A_2 \cdot \text{Fleet Size} + A_3 \cdot \text{Commodity Mix} + A_4 \cdot \text{Loss History} + A_5 \cdot \text{Geography} + ... A_n \cdot \text{Operations} \end{equation} \begin{equation} F_{\text{risk}} = \sum_{i=1}^{n} A_i X_i \end{equation}Where each coefficient is a real number determined by actuarial analysis of your portfolio once a statistically significant sample of data has been recorded:
\[ A_i \in [0,1] \]And each Factor: \[X_i\] is collected at time of application.
Business Impact
Pricing Precision
Empirical coefficients eliminate subjective bias and create consistent pricing.
Portfolio Intelligence
Real-time dashboards track concentration, drift, and emerging risk.
Strategic Triage
High-fit accounts surface automatically; poor-fit risks are filtered instantly.
Reduced Volatility
Empirical consistency stabilizes loss ratios.
Compounding Intelligence
Every submission strengthens the model—regardless of whether it binds.
Conclusion
Fleetidy transforms underwriting from a subjective art into a measurable science. By retaining all submissions and applying a mathematical equation of risk, insurers gain:
- accuracy
- transparency
- consistency
- strategic insight
- competitive advantage
Fleetidy makes cargo risk quantifiable, knowable, and actionable.